20.500.12556/RUL-174674 Few-shot identification of individuals in sports the case of darts This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimaldata scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. biomechanical feedback few-shot learning open-set classification person recognition sensors sports unsupervised learning biomehanska povratna informacija učenje z malo primeri klasifikacija z odprtim naborom razredov prepoznava oseb senzorji šport nenadzorovano učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-08 14:08:14 2025-10-08 14:08:20 2025-11-27 03:32:47 0000-00-00 00:00:00 2025 0 0 20 str. issue 10,art. 865 Vol. 16 Oct. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.39:681.5 2078-2489 252329219 information-16-00865.pdf information-16-00865.pdf 1 4AC612D8D7D0F76F2FFB982F0F485A69 4ce32bbda0d4320e86c2c9dbd6b21879a234a0940e69fef12f868adbfb9dc36c 7793d3c0-a43f-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219544 https://www.mdpi.com/2078-2489/16/10/865 1 3f9e7b2c-a43f-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=219543 Fakulteta za elektrotehniko 0 0 0